Arno Candel

Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O’s Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms.

Cliff Click

Cliff Click is the CTO and Co-Founder of H2O, makers of H2O, the opensource math and machine learning engine for Big Data. Cliff wrote his first compiler when he was 15 (Pascal to TRS Z-80!), although Cliff’s most famous compiler is the HotSpot Server Compiler (the Sea of Nodes IR). Cliff helped Azul Systems build an 864 core pure-Java mainframe that keeps GC pauses on 500Gb heaps to under 10ms, and worked on all aspects of that JVM.

Mark Landry

Mark Landry is a competition data scientist and product manager at H2O. He enjoys testing ideas in Kaggle competitions, where he is ranked in the top 100 in the world (top 0.03%) and well-trained in getting quick solutions to iterate over. Most at home in SQL, he found H2O through hacking in R. Interests are multi-model architectures and helping the world make fewer models that perform worse than the mean.

Erin LeDell

Erin is a Statistician and Machine Learning Scientist at H2O.ai. She is the main author of H2O Ensemble. Before joining H2O, she was the Principal Data Scientist at Wise.io and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc.

Tomas Nykodym

Tomas is our resident Software Engineer. He received his Masters degree from the Czech Technical University. Tomas has worked at IBM-research and Agent-Technology Group. He has participated on several projects related to malware detection/protection funded by US Air Force. Specifically, he developed a system for modeling software behavior using compressed graphs of the system calls made on the system.

Rob Tibshirani

Robert Tibshirani's main interests are in applied statistics, biostatistics, and data mining. He is co-author of the books Generalized Additive Models (with T. Hastie), An Introduction to the Bootstrap (with B. Efron), and Elements of Statistical Learning (with T. Hastie and J. Friedman). His current research focuses on problems in biology and genomics, medicine, and industry. With collaborator Balasubramanian Narasimhan, he also develops software packages for genomics and proteomics.

Madeleine Udell

Madeleine Udell is a postdoctoral fellow at Caltech's Center for the Mathematics of Information, hosted by Joel Tropp. She will be joining Cornell as an Assistant Professor in the School of Operations Research and Information Engineering in July 2016. Her research focus is on modeling and solving large-scale optimization problems and on finding and exploiting structure in high dimensional data, with applications in marketing, demographic modeling, and medical informatics.